Visualisation_R4DS

Introduction

library(tidyverse)

Exercise 2

Run ggplot(data = mpg) what do you see?

ggplot(data = mpg)

This code creates an empty plot. The ggplot() function creates the background of the plot, but since no layers were specified with geom function, nothing is drawn.

Exercise 3.2.2

How many rows are in mpg? How many columns?

There are 234 rows and 11 columns in the mpg data frame.

nrow(mpg)

## [1] 234

ncol(mpg)

## [1] 11

The glimpse() function also displays the number of rows and columns in a data frame.

glimpse(mpg)

## Rows: 234
## Columns: 11
## $ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi", …
## $ model        <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", …
## $ displ        <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2.0, 2…
## $ year         <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, 20…
## $ cyl          <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, 8,…
## $ trans        <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "aut…
## $ drv          <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "4", "…
## $ cty          <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17, 17, …
## $ hwy          <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25, 25, …
## $ fl           <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "…
## $ class        <chr> "compact", "compact", "compact", "compact", "compact", "…

Exercise 3.2.3

What does the drv variable describe? Read the help for ?mpg to find out.

The drv variable is a categorical variable which categorizes cars into front-wheels, rear-wheels, or four-wheel drive.

Exercise 3.2.4

Make a scatter plot of hwy vs. cyl.

ggplot(mpg, aes(x = cyl, y = hwy)) +
  geom_point()

Exercise 3.2.5

What happens if you make a scatter plot of class vs drv? Why is the plot not useful?

The resulting scatter plot has only a few points.

ggplot(mpg, aes(x = class, y = drv)) +
  geom_point()

A scatter plot is not a useful display of these variables since both drv and class are categorical variables. Since categorical variables typically take a small number of values, there are a limited number of unique combinations of (x, y) values that can be displayed. In this data, drv takes 3 values and class takes 7 values, meaning that there are only 21 values that could be plotted on a scatterplot of drv vs. class. In this data, there 12 values of (drv, class) are observed.

count(mpg, drv, class)

## # A tibble: 12 x 3
##    drv   class          n
##    <chr> <chr>      <int>
##  1 4     compact       12
##  2 4     midsize        3
##  3 4     pickup        33
##  4 4     subcompact     4
##  5 4     suv           51
##  6 f     compact       35
##  7 f     midsize       38
##  8 f     minivan       11
##  9 f     subcompact    22
## 10 r     2seater        5
## 11 r     subcompact     9
## 12 r     suv           11

A simple scatter plot does not show how many observations there are for each (x, y) value. As such, scatter plots work best for plotting a continuous x and a continuous y variable, and when all (x, y) values are unique.

ggplot(mpg, aes(x = class, y = drv)) +
  geom_count()

Aesthetic mappings

Exercise 3.3.1

What’s gone wrong with this code? Why are the points not blue?

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, colour = "blue"))

The argumentcolour = "blue" is included within the mapping argument, and as such, it is treated as an aesthetic, which is a mapping between a variable and a value. In the expression, colour = "blue", "blue" is interpreted as a categorical variable which only takes a single value "blue".

The following code does produces the expected result.

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy), colour = "blue")

Exercise 3.3.2

Which variables in mpg are categorical? Which variables are continuous? (Hint: type ?mpg to read the documentation for the dataset). How can you see this information when you run mpg?

The following list contains the categorical variables in mpg:

• manufacturer
• model
• trans
• drv
• fl
• class

The following list contains the continuous variables in mpg:

• displ
• year
• cyl
• cty
• hwy

In the printed data frame, angled brackets at the top of each column provide type of each variable. there are two types of categorical variables: nominal and ordinal

mpg

## # A tibble: 234 x 11
##    manufacturer model    displ  year   cyl trans   drv     cty   hwy fl    class
##    <chr>        <chr>    <dbl> <int> <int> <chr>   <chr> <int> <int> <chr> <chr>
##  1 audi         a4         1.8  1999     4 auto(l… f        18    29 p     comp…
##  2 audi         a4         1.8  1999     4 manual… f        21    29 p     comp…
##  3 audi         a4         2    2008     4 manual… f        20    31 p     comp…
##  4 audi         a4         2    2008     4 auto(a… f        21    30 p     comp…
##  5 audi         a4         2.8  1999     6 auto(l… f        16    26 p     comp…
##  6 audi         a4         2.8  1999     6 manual… f        18    26 p     comp…
##  7 audi         a4         3.1  2008     6 auto(a… f        18    27 p     comp…
##  8 audi         a4 quat…   1.8  1999     4 manual… 4        18    26 p     comp…
##  9 audi         a4 quat…   1.8  1999     4 auto(l… 4        16    25 p     comp…
## 10 audi         a4 quat…   2    2008     4 manual… 4        20    28 p     comp…
## # … with 224 more rows

Those with <chr> above their columns are categorical, while those with <dbl> or <int> are continuous.

glimpse() is another function that concisely displays the type of each column in the data frame:

glimpse(mpg)

## Rows: 234
## Columns: 11
## $ manufacturer <chr> "audi", "audi", "audi", "audi", "audi", "audi", "audi", …
## $ model        <chr> "a4", "a4", "a4", "a4", "a4", "a4", "a4", "a4 quattro", …
## $ displ        <dbl> 1.8, 1.8, 2.0, 2.0, 2.8, 2.8, 3.1, 1.8, 1.8, 2.0, 2.0, 2…
## $ year         <int> 1999, 1999, 2008, 2008, 1999, 1999, 2008, 1999, 1999, 20…
## $ cyl          <int> 4, 4, 4, 4, 6, 6, 6, 4, 4, 4, 4, 6, 6, 6, 6, 6, 6, 8, 8,…
## $ trans        <chr> "auto(l5)", "manual(m5)", "manual(m6)", "auto(av)", "aut…
## $ drv          <chr> "f", "f", "f", "f", "f", "f", "f", "4", "4", "4", "4", "…
## $ cty          <int> 18, 21, 20, 21, 16, 18, 18, 18, 16, 20, 19, 15, 17, 17, …
## $ hwy          <int> 29, 29, 31, 30, 26, 26, 27, 26, 25, 28, 27, 25, 25, 25, …
## $ fl           <chr> "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "p", "…
## $ class        <chr> "compact", "compact", "compact", "compact", "compact", "…

Exercise 3.3.3

Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables?

The variable cty, city highway miles per gallon, is a continuous variable.

ggplot(mpg, aes(x = displ, y = hwy, colour = cty)) +
  geom_point()

Instead of using discrete colors, the continuous variable uses a scale that varies from a light to dark blue color.

ggplot(mpg, aes(x = displ, y = hwy, size = cty)) +
  geom_point()

When mapped to size, the sizes of the points vary continuously as a function of their size.

ggplot(mpg, aes(x = displ, y = hwy, shape = cty)) +
  geom_point()

## Error: A continuous variable can not be mapped to shape

When a continuous value is mapped to shape, it gives an error. A numeric variable has an order, but shapes do not. It is clear that smaller points correspond to smaller values, or once the color scale is given, which colors correspond to larger or smaller values. But it is not clear whether a square is greater or less than a circle.

Exercise 3.3.4

What happens if you map the same variable to multiple aesthetics?

ggplot(mpg, aes(x = displ, y = hwy, colour = hwy, size = displ)) +
  geom_point()

In the above plot, hwy is mapped to both location on the y-axis and color, and displ is mapped to both location on the x-axis and size. The code works and produces a plot, even if it is a bad one. Mapping a single variable to multiple aesthetics is redundant. Because it is redundant information, in most cases avoid mapping a single variable to multiple aesthetics.

Exercise 3.3.5

What does the stroke aesthetic do? What shapes does it work with? (Hint: use ?geom_point)

Stroke changes the size of the border for shapes (21-25). These are filled shapes in which the color and size of the border can differ from that of the filled interior of the shape.

For example

ggplot(mtcars, aes(wt, mpg)) +
  geom_point(shape = 21, colour = "blue", fill = "yellow", size = 5, stroke = 5)

Exercise 3.3.6

What happens if you map an aesthetic to something other than a variable name, like aes(colour = displ < 5)?

ggplot(mpg, aes(x = displ, y = hwy, colour = displ < 5)) +
  geom_point()

In this case, the result of displ < 5 is a logical variable which takes values of TRUE or FALSE. and ggplot() function creates a categorical variable.

Facets

Exercise 3.5.1

What happens if you facet on a continuous variable?

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  facet_wrap(. ~ cty)

The continuous variable is converted to a categorical variable, and the plot contains a facet for each distinct value.

Exercise 3.5.2

What do the empty cells in plot with facet_grid(drv ~ cyl) mean? How do they relate to this plot?

ggplot(data = mpg) +
  geom_point(mapping = aes(x = drv, y = cyl))

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(drv ~ cyl)

The empty cells (facets) in this plot are combinations of drv and cyl that have no observations. These are the same locations in the scatter plot of drv and cyl that have no points.

Exercise 3.5.3

What plots does the following code make? What does . do?

The symbol . ignores that dimension when faceting. For example, drv ~ . facet by values of drv on the y-axis.

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid( drv ~ .)

While, . ~ cyl will facet by values of cyl on the x-axis.

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_grid(. ~ cyl)

Exercise 3.5.4

Take the first faceted plot in this section:

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy)) +
  facet_wrap(~class, nrow = 2)

What are the advantages to using faceting instead of the colour aesthetic? What are the disadvantages? How might the balance change if you had a larger dataset?

In the following plot the class variable is mapped to color.

ggplot(data = mpg) +
  geom_point(mapping = aes(x = displ, y = hwy, color = class))

Advantages of encoding class with facets instead of colour include the ability to encode more distinct categories. it is difficult to distinguish between the colours of different categories when using class in colour. As the number of categories increases, the difference between colours decreases, to the point that the colour of categories will no longer be visually distinct. Disadvantages of encoding the class variable with facets instead of the colour aesthetic include the difficulty of comparing the values of observations between categories since the observations for each category are on different plots.

Exercise 3.5.5

Read ?facet_wrap. What does nrow do? What does ncol do? What other options control the layout of the individual panels? Why doesn’t facet_grid() have nrow and ncol variables?

The arguments nrow (ncol) determines the number of rows (columns) to use when laying out the facets. It is necessary since facet_wrap() only facets on one variable.

The nrow and ncol arguments are unnecessary for facet_grid() since the number of unique values of the variables specified in the function determines the number of rows and columns.

Exercise 3.5.6

When using facet_grid() you should usually put the variable with more unique levels in the columns. Why?

There will be more space for columns if the plot is laid out horizontally (landscape).

Geometric objects

Exercise 3.6.1

What geom would you use to draw a line chart? A boxplot? A histogram? An area chart?

• line chart: geom_line()
• boxplot: geom_boxplot()
• histogram: geom_histogram()
• area chart: geom_area()

Exercise 3.6.2

Run this code in your head and predict what the output will look like. Then, run the code in R and check your predictions.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, colour = drv)) +
  geom_point() +
  geom_smooth(se = FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

This code produces a scatter plot with displ on the x-axis, hwy on the y-axis, and the points coloured by drv. There will be a smooth line, without standard errors, fit through each drv group.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, colour = drv)) +
  geom_point() +
  geom_smooth(se = FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Exercise 3.6.3

What does show.legend = FALSE do? What happens if you remove it?

Answer

The theme option show.legend = FALSE hides the legend box.

Consider this example earlier in the chapter.

ggplot(data = mpg) +
  geom_smooth(
    mapping = aes(x = displ, y = hwy, colour = drv),
    show.legend = FALSE
    
  )

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

In that plot, there is no legend. Removing the show.legend argument or setting show.legend = TRUE will result in the plot having a legend displaying the mapping between colours and drv.

ggplot(data = mpg) +
  geom_smooth(mapping = aes(x = displ, y = hwy, colour = drv))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

In the chapter, the legend is suppressed because with three plots, adding a legend to only the last plot would make the sizes of plots different. Different sized plots would make it more difficult to see how arguments change the appearance of the plots. The purpose of those plots is to show the difference between no groups, using a group aesthetic, and using a colour aesthetic, which creates implicit groups. In that example, the legend isn’t necessary since looking up the values associated with each colour isn’t necessary to make that point.

Exercise 3.6.4

What does the se argument to geom_smooth() do?

It adds standard error bands to the lines.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, colour = drv)) +
  geom_point() +
  geom_smooth(se = TRUE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

By default se = TRUE:

ggplot(data = mpg, mapping = aes(x = displ, y = hwy, colour = drv)) +
  geom_point() +
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Exercise 3.6.5

Will these two graphs look different? Why/why not?

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth()

ggplot() +
  geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))

No. Because both geom_point() and geom_smooth() will use the same data and mappings. They will inherit those options from the ggplot() object, so the mappings don’t need to specified again.

ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot() +
  geom_point(data = mpg, mapping = aes(x = displ, y = hwy)) +
  geom_smooth(data = mpg, mapping = aes(x = displ, y = hwy))

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Exercise 3.6.6

Recreate the R code necessary to generate the following graphs. Please find the graphs in the R4DS book in the relevant chapter exercises.

The following code will generate those plots.

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point() +
  geom_smooth(se = FALSE)

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_smooth(mapping = aes(group = drv), se = FALSE) +
  geom_point()

ggplot(mpg, aes(x = displ, y = hwy, colour = drv)) +
  geom_point() +
  geom_smooth(se = FALSE)

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(colour = drv)) +
  geom_smooth(se = FALSE)

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(aes(colour = drv)) +
  geom_smooth(aes(linetype = drv), se = FALSE)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

ggplot(mpg, aes(x = displ, y = hwy)) +
  geom_point(size = 4, color = "white") +
  geom_point(aes(colour = drv))

Statistical transformations

Exercise 3.7.1

What is the default geom associated with stat_summary()? How could you rewrite the previous plot to use that geom function instead of the stat function?

The “previous plot” referred to in the question is the following.

ggplot(data = diamonds) +
  stat_summary(
    mapping = aes(x = cut, y = depth),
    fun.min = min,
    fun.max = max,
    fun = median
  )

The default geom for stat_summary() is geom_pointrange(). The default stat for geom_pointrange() is identity() but we can add the argument stat = "summary" to use stat_summary() instead of stat_identity().

ggplot(data = diamonds) +
  geom_pointrange(
    mapping = aes(x = cut, y = depth),
    stat = "summary"
  )

## No summary function supplied, defaulting to `mean_se()`

The resulting message says that stat_summary() uses the mean and sd to calculate the middle point and endpoints of the line. However, in the original plot the min and max values were used for the endpoints. To recreate the original plot we need to specify values for fun.min, fun.max, and fun.

ggplot(data = diamonds) +
  geom_pointrange(
    mapping = aes(x = cut, y = depth),
    stat = "summary",
    fun.min = min,
    fun.max = max,
    fun = median
  )

Exercise 3.7.2

What does geom_col() do? How is it different to geom_bar()?

The geom_col() function has different default stat than geom_bar(). The default stat of geom_col() is stat_identity(), which leaves the data as it is. The geom_col() function expects that the data contains x values and y values which represent the bar height.

The default stat of geom_bar() is stat_count(). The geom_bar() function only expects an x variable.

Exercise 3.7.3

Most geoms and stats come in pairs that are almost always used in concert. Read through the documentation and make a list of all the pairs. What do they have in common?

The following tables lists the pairs of geoms and stats that are almost always used in concert.

Complementary geoms and stats

geom	stat
geom_bar()	stat_count()
geom_bin2d()	stat_bin_2d()
geom_boxplot()	stat_boxplot()
geom_contour_filled()	stat_contour_filled()
geom_contour()	stat_contour()
geom_count()	stat_sum()
geom_density_2d()	stat_density_2d()
geom_density()	stat_density()
geom_dotplot()	stat_bindot()
geom_function()	stat_function()
geom_sf()	stat_sf()
geom_sf()	stat_sf()
geom_smooth()	stat_smooth()
geom_violin()	stat_ydensity()
geom_hex()	stat_bin_hex()
geom_qq_line()	stat_qq_line()
geom_qq()	stat_qq()
geom_quantile()	stat_quantile()

These pairs of geoms and stats tend to have their names in common, such stat_smooth() and geom_smooth() and be documented on the same help page. The pairs of geoms and stats that are used in concert often have each other as the default stat (for a geom) or geom (for a stat).

The following tables contain the geoms and stats in ggplot2 and their defaults as of version 3.3.0. Many geoms have stat_identity() as the default stat.

ggplot2 geom layers and their default stats.

geom	default stat	shared docs
geom_abline()	stat_identity()
geom_area()	stat_identity()
geom_bar()	stat_count()	x
geom_bin2d()	stat_bin_2d()	x
geom_blank()	None
geom_boxplot()	stat_boxplot()	x
geom_col()	stat_identity()
geom_count()	stat_sum()	x
geom_countour_filled()	stat_countour_filled()	x
geom_countour()	stat_countour()	x
geom_crossbar()	stat_identity()
geom_curve()	stat_identity()
geom_density_2d_filled()	stat_density_2d_filled()	x
geom_density_2d()	stat_density_2d()	x
geom_density()	stat_density()	x
geom_dotplot()	stat_bindot()	x
geom_errorbar()	stat_identity()
geom_errorbarh()	stat_identity()
geom_freqpoly()	stat_bin()	x
geom_function()	stat_function()	x
geom_hex()	stat_bin_hex()	x
geom_histogram()	stat_bin()	x
geom_hline()	stat_identity()
geom_jitter()	stat_identity()
geom_label()	stat_identity()
geom_line()	stat_identity()
geom_linerange()	stat_identity()
geom_map()	stat_identity()
geom_path()	stat_identity()
geom_point()	stat_identity()
geom_pointrange()	stat_identity()
geom_polygon()	stat_identity()
geom_qq_line()	stat_qq_line()	x
geom_qq()	stat_qq()	x
geom_quantile()	stat_quantile()	x
geom_raster()	stat_identity()
geom_rect()	stat_identity()
geom_ribbon()	stat_identity()
geom_rug()	stat_identity()
geom_segment()	stat_identity()
geom_sf_label()	stat_sf_coordinates()	x
geom_sf_text()	stat_sf_coordinates()	x
geom_sf()	stat_sf()	x
geom_smooth()	stat_smooth()	x
geom_spoke()	stat_identity()
geom_step()	stat_identity()
geom_text()	stat_identity()
geom_tile()	stat_identity()
geom_violin()	stat_ydensity()	x
geom_vline()	stat_identity()

ggplot2 stat layers and their default geoms.

stat	default geom	shared docs
stat_bin_2d()	geom_tile()
stat_bin_hex()	geom_hex()	x
stat_bin()	geom_bar()	x
stat_boxplot()	geom_boxplot()	x
stat_count()	geom_bar()	x
stat_countour_filled()	geom_contour_filled()	x
stat_countour()	geom_contour()	x
stat_density_2d_filled()	geom_density_2d()	x
stat_density_2d()	geom_density_2d()	x
stat_density()	geom_area()
stat_ecdf()	geom_step()
stat_ellipse()	geom_path()
stat_function()	geom_function()	x
stat_function()	geom_path()
stat_identity()	geom_point()
stat_qq_line()	geom_path()
stat_qq()	geom_point()
stat_quantile()	geom_quantile()	x
stat_sf_coordinates()	geom_point()
stat_sf()	geom_rect()
stat_smooth()	geom_smooth()	x
stat_sum()	geom_point()
stat_summary_2d()	geom_tile()
stat_summary_bin()	geom_pointrange()
stat_summary_hex()	geom_hex()
stat_summary()	geom_pointrange()
stat_unique()	geom_point()
stat_ydensity()	geom_violin()	x

Exercise 3.7.4

What variables does stat_smooth() compute? What parameters control its behavior?

The function stat_smooth() calculates the following variables:

• y: predicted value
• ymin: lower value of the confidence interval
• ymax: upper value of the confidence interval
• se: standard error

The “Computed Variables” section of the stat_smooth() documentation contains these variables.

The parameters that control the behaviour of stat_smooth() include:

• method: This is the method used to compute the smoothing line. If NULL, a default method is used based on the sample size: stats::loess() when there are less than 1,000 observations in a group, and mgcv::gam() with formula = y ~ s(x, bs = "CS) otherwise. Alternatively, the user can provide a character vector with a function name, e.g. "lm", "loess", or a function, e.g. MASS::rlm.

• formula: When providing a custom method argument, the formula to use. The default is y ~ x. For example, to use the line implied by lm(y ~ x + I(x ^ 2) + I(x ^ 3)), use method = "lm" or method = lm and formula = y ~ x + I(x ^ 2) + I(x ^ 3).

• method.arg(): Arguments other than than the formula, which is already specified in the formula argument, to pass to the function inmethod`.

• se: If TRUE, display standard error bands, if FALSE only display the line.

• na.rm: If FALSE, missing values are removed with a warning, if TRUE the are silently removed. The default is FALSE in order to make debugging easier. If missing values are known to be in the data, then can be ignored, but if missing values are not anticipated this warning can help catch errors.

Exercise 3.7.5

In our proportion bar chart, we need to set group = 1 Why? In other words, what is the problem with these two graphs?

If group = 1 is not included, then all the bars in the plot will have the same height, a height of 1. The function geom_bar() assumes that the groups are equal to the x values since the stat computes the counts within the group.

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, y = ..prop..))

The problem with these two plots is that the proportions are calculated within the groups.

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, y = ..prop..))

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, fill = color, y = ..prop..))

The following code will produce the intended stacked bar charts for the case with no fill aesthetic.

ggplot(data = diamonds) +
  geom_bar(mapping = aes(x = cut, y = ..prop.., group = 1))

Position adjustments

Exercise 3.8.1

What is the problem with this plot? How could you improve it?

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point()

There is overplotting because there are multiple observations for each combination of cty and hwy values.

I would improve the plot by using a jitter position adjustment to decrease overplotting.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point(position = "jitter")

The relationship between cty and hwy is clear even without jittering the points but jittering shows the locations where there are more observations.

Exercise 3.8.2

What parameters to geom_jitter() control the amount of jittering?

From the geom_jitter() documentation, there are two arguments to jitter:

• width controls the amount of horizontal displacement, and
• height controls the amount of vertical displacement.

The defaults values of width and height will introduce noise in both directions.

Exercise 3.8.3

Compare and contrast geom_jitter() with geom_count().

The geom geom_jitter() adds random variation to the locations points of the graph. In other words, it “jitters” the locations of points slightly. This method reduces overplotting since two points with the same location are unlikely to have the same random variation.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_jitter()

However, the reduction in overlapping comes at the cost of slightly changing the x and y values of the points. It might make your graph less accurate at small scales but it makes the graph more revealing at large scale.

The geom geom_count() sizes the points relative to the number of observations. Combinations of (x, y) values with more observations will be larger than those with fewer observations.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_count()

The geom_count() geom does not change x and y coordinates of the points. However, if the points are close together and counts are large, the size of some points can itself create overplotting. For example, in the following example, a third variable mapped to colour is added to the plot. In this case, geom_count() is less readable than geom_jitter() when adding a third variable as a colour aesthetic.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy, color = class)) +
  geom_jitter()

ggplot(data = mpg, mapping = aes(x = cty, y = hwy, color = class)) +
  geom_count()

Combining geom_count() with jitter, which is specified with the position argument to geom_count() rather than its own geom, helps overplotting a little.

ggplot(data = mpg, mapping = aes(x = cty, y = hwy, color = class)) +
  geom_count(position = "jitter")

But as this example shows, unfortunately, there is no universal solution to overplotting. The costs and benefits of different approaches will depend on the structure of the data and the goal of the data scientist.

Exercise 3.8.4

What’s the default position adjustment for geom_boxplot()? Create a visualization of the mpg dataset that demonstrates it.

The default position for geom_boxplot() is "dodge2", which is a shortcut for position_dodge2. This position adjustment does not change the vertical position of a geom but moves the geom horizontally to avoid overlapping other geoms. See the documentation for position_dodge2() for additional discussion on how it works.

When we add colour = class to the box plot, the different levels of the drv variable are placed side by side, i.e., dodged.

ggplot(data = mpg, aes(x = drv, y = hwy, colour = class)) +
  geom_boxplot()

Coordinate systems

Exercise 3.9.1

Turn a stacked bar chart into a pie chart using coord_polar().

A pie chart is a stacked bar chart with the addition of polar coordinates. Take this stacked bar chart with a single category.

ggplot(data = diamonds) + 
  geom_bar(mapping = aes(x = cut, fill = clarity)) +
  coord_polar()

Exercise 3.9.2

What does labs() do? Read the documentation.

The labs function adds axis titles, plot titles, and a caption to the plot.

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
  geom_boxplot() +
  coord_flip() +
  labs(y = "Highway MPG",
       x = "Class",
       title = "Highway MPG by car class",
       subtitle = "1999-2008",
       caption = "Source: http://fueleconomy.gov")

The arguments to labs() are optional, so you can add as many or as few of these as are needed.

ggplot(data = mpg, mapping = aes(x = class, y = hwy)) +
  geom_boxplot() +
  coord_flip() +
  labs(y = "Highway MPG",
       x = "Class",
       title = "Highway MPG by car class")

The labs() function is not the only function that adds titles to plots. The xlab(), ylab(), and x- and y-scale functions can add axis titles. The ggtitle() function adds plot titles.

Exercise 3.9.3

What’s the difference between coord_quickmap() and coord_map()?

The coord_map() function uses map projections to project the three-dimensional Earth onto a two-dimensional plane. By default, coord_map() uses the Mercator projection. This projection is applied to all the geoms in the plot. The coord_quickmap() function uses an approximate but faster map projection. This approximation ignores the curvature of Earth and adjusts the map for the latitude/longitude ratio. The coord_quickmap() project is faster than coord_map() both because the projection is computationally easier, and unlike coord_map(), the coordinates of the individual geoms do not need to be transformed.

See the coord_map() documentation for more information on these functions and some examples.

Exercise 3.9.4

What does the plot below tell you about the relationship between city and highway mpg? Why is coord_fixed() important? What does geom_abline() do?

The function coord_fixed() ensures that the line produced by geom_abline() is at a 45-degree angle. A 45-degree line makes it easy to compare the highway and city mileage to the case in which city and highway MPG were equal.

p <- ggplot(data = mpg, mapping = aes(x = cty, y = hwy)) +
  geom_point() +
  geom_abline()
p + coord_fixed()

If we didn’t include coord_fixed(), then the line would no longer have an angle of 45 degrees.

p

On average, humans are best able to perceive differences in angles relative to 45 degrees. See @Cleveland1993, @Cleveland1994,@Cleveland1993a, @ClevelandMcGillMcGill1988, @HeerAgrawala2006 for discussion on how the aspect ratio of a plot affects perception of the values it encodes, evidence that 45-degrees is generally the optimal aspect ratio, and methods to calculate the optimal aspect ratio of a plot. The function ggthemes::bank_slopes() will calculate the optimal aspect ratio to bank slopes to 45-degrees.